..

AAAI2021/paper.tex

@@ -175,37 +175,37 @@
 \maketitle
 
 \begin{abstract}
- The emergence of symbolic languages with high compositionality has
-  attracted extensive attention from a broad range of communities. Existing
-  studies achieve high compositionality through \emph{deliberately handcrafted}
-  inductions (e.g., addtional rewards, constructed 
-  loss functions and ease-of-teaching) in multi-agent learning, which are unnatural. 
-  Yet, few studies investigate the emergence of symbolic language with high
-  compositionality \emph{naturally}, i.e., without deliberately handcrafted
-  inductions. 
-  
-  In this paper, \note{we are the first to successfully achieve high compositional
-  symbolic language} in a \emph{natural} manner without handcrafted inductions.
-  Initially, by investigating the emerged symbolic
-  language after removing the \emph{deliberately handcrafted}
-  inductions, we observe the difficulty in naturally generating high compositional
-  symbolic language.
-  %the agent capacity plays a key role in compositionality.
-  Further we reveal and characterize the \note{quantitative relationship}
-  between the agent capacity and the compositionality of symbolic language, with
-  a novel mutual information-based metric for more reasonable measuring the compositionality. 
-  % both theoretically and experimentally.
-  %The theoretical analysis is built on the MSC
-  %(Markov Series Channel) model for the language transmission process and a
-  %novel mutual information-based metric for the compositionality.
-  %The
-  %experiments are conducted on a listener-speaker referential game framework
-  %with eliminated external environment factors.
-  %With a novel mutual information-based metric for the compositionality, 
-  The experimental results lead to a counter-intuitive conclusion that lower agent
-  capacity facilitates the emergence of symbolic language with higher
-  compositionality. \note{Based on our conclusion, we can generate higher
-  compositional symbolic language with a higher probability.}
+The emergence of symbolic languages with high compositionality has
+attracted extensive attention from a broad range of communities. Existing
+studies achieve high compositionality through \emph{deliberately handcrafted}
+inductions (e.g., additional rewards, constructed
+loss functions and ease-of-teaching) in multi-agent learning, which are unnatural.
+Yet, few studies investigate the emergence of symbolic language with high
+compositionality \emph{naturally}, i.e., without deliberately handcrafted
+inductions.
+
+In this paper, \note{we are the first to successfully achieve high compositional
+symbolic language} in a \emph{natural} manner without handcrafted inductions.
+Initially, by investigating the emerged symbolic
+language after removing the \emph{deliberately handcrafted}
+inductions, we observe the difficulty in naturally generating high compositional
+symbolic language.
+%the agent capacity plays a key role in compositionality.
+Further, we reveal and characterize the \note{quantitative relationship}
+between the agent capacity and the compositionality of symbolic language, with
+a novel mutual information-based metric for more reasonable measuring the compositionality.
+% both theoretically and experimentally.
+%The theoretical analysis is built on the MSC
+%(Markov Series Channel) model for the language transmission process and a
+%novel mutual information-based metric for the compositionality.
+%The
+%experiments are conducted on a listener-speaker referential game framework
+%with eliminated external environment factors.
+%With a novel mutual information-based metric for the compositionality,
+The experimental results lead to a counter-intuitive conclusion that lower agent
+capacity facilitates the emergence of symbolic language with higher
+compositionality. \note{Based on our conclusion, we can generate higher
+compositional symbolic language with a higher probability.}
 
 
 %  The natural emergence of symbolic languages with high compositionality has

AAAI2021/tex/introduction.tex

@@ -2,11 +2,11 @@
 \label{sec:introduction}
 
 The emergence of symbolic language has always been an important issue,
-which attracts attentions from a broad range of communities,
+which attracts attention from a broad range of communities,
 including philology~\cite{}, biology~\cite{}, and computer
-science~\cite{}. Especially in computer science, efforts in recent years try to explore
+science~\cite{}. Especially in computer science, efforts in recent years trying to explore
 the emergence of symbolic language in virtual multi-agent environments, where
-agents are trained to communicate with neural network based methods such as deep
+agents are trained to communicate with neural-network-based methods such as deep
 reinforcement learning~\cite{}.
 %Such works can be roughly classified into two categories,
 %referential game~\cite{} and multi-agent reinforcement learning (MARL)~\cite{}, based on
@@ -21,7 +21,7 @@ given set of simple components (e.g., symbols), can be determined by its
 constituent components and the rule combining them~\cite{}.
 \note{For example, the expression "AAAI is a conference'' consists of two
 meaningful words ``AAAI'' and ``conference'', and a rule for definition (``is'').
-Compositionality is considered to be a source of the productivity,
+Compositionality is considered to be a source of productivity,
 systematicity, and learnability of language, and the reason why a language with finite
 vocabulary can express almost infinite concepts.}
 %More recently, measuring the compositionality \note{xxxxx}.}
@@ -50,13 +50,13 @@ vocabulary can express almost infinite concepts.}
 
 Prior studies focus on achieving high compositional symbolic language
 through \emph{deliberately handcrafted} inductions, e.g., small vocabulary
-sizes~\cite{}, memoryless~\cite{}, addtional rewards~\cite{}, constructed loss functions~\cite{}, and
-ease-of-teaching~\cite{}. \note{Such optimization methodologies are driven by the challenges to generate high compositional symbolic without induction in existing multi-agent environment.}
+sizes~\cite{}, memoryless~\cite{}, additional rewards~\cite{}, constructed loss functions~\cite{}, and
+ease-of-teaching~\cite{}. \note{Such optimization methodologies are driven by the challenges to generate high compositional symbolic without induction in an existing multi-agent environment.}
 Figure~\ref{fig:induction} reports the compositionality when training two agents
 in the widely-used listener-speaker referential game for emerging 100 symbolic
 languages, and it can be observed that \note{the compositionality
 of emerged symbolic language is extremely low without any induction. Moreover, varying
-    the vocabulary size does not affect the compositionality notably.}
+the vocabulary size does not affect the compositionality notably.}
 Though such unnatural inductions are useful, they prevent us from better understanding the mystery of
 the emergence of language and even intelligence among our pre-human ancestors.
 Yet, few works investigate the emergence of high compositional symbolic language
@@ -184,16 +184,16 @@ In this paper, we made the following contributions:
 \begin{itemize}[topsep=0pt,itemsep=0cm]
 \item To our best knowledge, we are the first work to successfully achieve
 high compositional symbolic
-  language naturally, without any deliberately handcrafted induction.
+language naturally, without any deliberately handcrafted induction.
 \item We analyze the compositionality of emerged symbolic language
-  after removing deliberately handcrafted inductions.
+after removing deliberately handcrafted inductions.
 \item We propose a novel mutual information-based metric to measure the
-  compositionality quantitatively, which is more reasonable.
+compositionality quantitatively, which is more reasonable.
 \item We experimentally exploited the relationship between agent
-  capacity. Both theoretical analysis and
-  experimental results lead to a counter-intuitive conclusion that lower agent
-  capacity facilitates the emergence of symbolic language with higher
-  compositionality.
+capacity. Both theoretical analysis and
+experimental results lead to a counter-intuitive conclusion that lower agent
+capacity facilitates the emergence of symbolic language with higher
+compositionality.
 \end{itemize}
 
 
@@ -201,7 +201,7 @@ The rest of this paper is arranged as follows.
 Section~\ref{sec:relatedwork} summarizes the related works.
 Section~\ref{sec:thory}
 introduces the experimental setup used in this study. Section~\ref{sec:mis}
-describes our proposed the novel mutual information-based metric for measuring
+describes our proposed novel mutual-information-based metric for measuring
 the compositionality of symbolic language. Section~\ref{sec:exp} gives the
 experimental results of the exploration for the relationship between agent
 capacity and compositionality. Section~\ref{sec:con} concludes this paper.